Overview

Dataset statistics

Number of variables20
Number of observations586672
Missing cells71
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory89.5 MiB
Average record size in memory160.0 B

Variable types

Categorical8
Numeric12

Alerts

id has a high cardinality: 586672 distinct valuesHigh cardinality
name has a high cardinality: 446474 distinct valuesHigh cardinality
artists has a high cardinality: 114030 distinct valuesHigh cardinality
id_artists has a high cardinality: 115062 distinct valuesHigh cardinality
release_date has a high cardinality: 19700 distinct valuesHigh cardinality
danceability is highly overall correlated with valenceHigh correlation
energy is highly overall correlated with loudness and 1 other fieldsHigh correlation
loudness is highly overall correlated with energy and 1 other fieldsHigh correlation
acousticness is highly overall correlated with energy and 1 other fieldsHigh correlation
valence is highly overall correlated with danceabilityHigh correlation
explicit is highly imbalanced (73.9%)Imbalance
time_signature is highly imbalanced (68.6%)Imbalance
id is uniformly distributedUniform
id has unique valuesUnique
popularity has 44690 (7.6%) zerosZeros
key has 74950 (12.8%) zerosZeros
instrumentalness has 205083 (35.0%) zerosZeros

Reproduction

Analysis started2023-01-27 15:29:04.905247
Analysis finished2023-01-27 15:30:26.214812
Duration1 minute and 21.31 seconds
Software versionpandas-profiling vv3.6.1
Download configurationconfig.json

Variables

id
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct586672
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.5 MiB
4iJyoBOLtHqaGxP12qzhQI
 
1
0lqbiUXCJa13jUeKWBLzR7
 
1
5nmX1lXd9FgQYnps1nlJ1D
 
1
5ZIlRm2M1kYTAYbAdaUt9R
 
1
32fzMeDGUCjMbHkkEIvOqo
 
1
Other values (586667)
586667 

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters12906784
Distinct characters62
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique586672 ?
Unique (%)100.0%

Sample

1st row4iJyoBOLtHqaGxP12qzhQI
2nd row7lPN2DXiMsVn7XUKtOW1CS
3rd row3Ofmpyhv5UAQ70mENzB277
4th row5QO79kh1waicV47BqGRL3g
5th row6tDDoYIxWvMLTdKpjFkc1B

Common Values

ValueCountFrequency (%)
4iJyoBOLtHqaGxP12qzhQI 1
 
< 0.1%
0lqbiUXCJa13jUeKWBLzR7 1
 
< 0.1%
5nmX1lXd9FgQYnps1nlJ1D 1
 
< 0.1%
5ZIlRm2M1kYTAYbAdaUt9R 1
 
< 0.1%
32fzMeDGUCjMbHkkEIvOqo 1
 
< 0.1%
0xh4MFBK2ZgtsllsGEKT4v 1
 
< 0.1%
3kBkVfWxmd00JedGLo99yX 1
 
< 0.1%
1YENdnBx3x5nqXQ29hkhSw 1
 
< 0.1%
0oikhcfak66Nb7Fy0peqJi 1
 
< 0.1%
4ecDGriXJaUIacB043RFAh 1
 
< 0.1%
Other values (586662) 586662
> 99.9%

Length

2023-01-27T23:30:26.349141image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
4ijyobolthqagxp12qzhqi 1
 
< 0.1%
7maibctli4iisctbhkrgmh 1
 
< 0.1%
35mvy5s1h3j2qzyna3tfe0 1
 
< 0.1%
5rubkoudopn5kj5tlvxsxy 1
 
< 0.1%
4cg7huwyhbv6r6thn1gxrl 1
 
< 0.1%
3ofmpyhv5uaq70menzb277 1
 
< 0.1%
5qo79kh1waicv47bqgrl3g 1
 
< 0.1%
6tddoyixwvmltdkpjfkc1b 1
 
< 0.1%
0vjijw4gluzamyd2vxmi3b 1
 
< 0.1%
7vrjn5hdsxrmdxor30kgf1 1
 
< 0.1%
Other values (586662) 586662
> 99.9%

Most occurring characters

ValueCountFrequency (%)
0 278639
 
2.2%
1 275299
 
2.1%
2 274969
 
2.1%
4 274047
 
2.1%
3 273494
 
2.1%
5 272518
 
2.1%
6 271098
 
2.1%
7 256601
 
2.0%
s 200017
 
1.5%
y 199765
 
1.5%
Other values (52) 10330337
80.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5174821
40.1%
Uppercase Letter 5157371
40.0%
Decimal Number 2574592
19.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 200017
 
3.9%
y 199765
 
3.9%
e 199651
 
3.9%
i 199593
 
3.9%
t 199436
 
3.9%
w 199423
 
3.9%
r 199363
 
3.9%
v 199352
 
3.9%
k 199288
 
3.9%
h 199229
 
3.8%
Other values (16) 3179704
61.4%
Uppercase Letter
ValueCountFrequency (%)
A 199714
 
3.9%
C 199535
 
3.9%
M 199508
 
3.9%
F 199310
 
3.9%
B 199193
 
3.9%
J 199101
 
3.9%
H 199084
 
3.9%
L 198897
 
3.9%
K 198842
 
3.9%
D 198680
 
3.9%
Other values (16) 3165507
61.4%
Decimal Number
ValueCountFrequency (%)
0 278639
10.8%
1 275299
10.7%
2 274969
10.7%
4 274047
10.6%
3 273494
10.6%
5 272518
10.6%
6 271098
10.5%
7 256601
10.0%
9 199538
7.8%
8 198389
7.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 10332192
80.1%
Common 2574592
 
19.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 200017
 
1.9%
y 199765
 
1.9%
A 199714
 
1.9%
e 199651
 
1.9%
i 199593
 
1.9%
C 199535
 
1.9%
M 199508
 
1.9%
t 199436
 
1.9%
w 199423
 
1.9%
r 199363
 
1.9%
Other values (42) 8336187
80.7%
Common
ValueCountFrequency (%)
0 278639
10.8%
1 275299
10.7%
2 274969
10.7%
4 274047
10.6%
3 273494
10.6%
5 272518
10.6%
6 271098
10.5%
7 256601
10.0%
9 199538
7.8%
8 198389
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12906784
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 278639
 
2.2%
1 275299
 
2.1%
2 274969
 
2.1%
4 274047
 
2.1%
3 273494
 
2.1%
5 272518
 
2.1%
6 271098
 
2.1%
7 256601
 
2.0%
s 200017
 
1.5%
y 199765
 
1.5%
Other values (52) 10330337
80.0%

name
Categorical

Distinct446474
Distinct (%)76.1%
Missing71
Missing (%)< 0.1%
Memory size4.5 MiB
Summertime
 
101
Intro
 
92
Year 3000
 
91
Hold On
 
87
2000 Years
 
76
Other values (446469)
586154 

Length

Max length529
Median length242
Mean length20.243994
Min length1

Characters and Unicode

Total characters11875147
Distinct characters4678
Distinct categories22 ?
Distinct scripts17 ?
Distinct blocks34 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique376093 ?
Unique (%)64.1%

Sample

1st rowPeaches (feat. Daniel Caesar & Giveon)
2nd rowdrivers license
3rd rowAstronaut In The Ocean
4th rowSave Your Tears
5th rowtelepatía

Common Values

ValueCountFrequency (%)
Summertime 101
 
< 0.1%
Intro 92
 
< 0.1%
Year 3000 91
 
< 0.1%
Hold On 87
 
< 0.1%
2000 Years 76
 
< 0.1%
Home 74
 
< 0.1%
Baby 72
 
< 0.1%
Stay 68
 
< 0.1%
Angel 68
 
< 0.1%
Forever 65
 
< 0.1%
Other values (446464) 585807
99.9%
(Missing) 71
 
< 0.1%

Length

2023-01-27T23:30:26.499940image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
118721
 
5.3%
the 39475
 
1.8%
in 21286
 
1.0%
a 21150
 
1.0%
i 18791
 
0.8%
de 17376
 
0.8%
you 17318
 
0.8%
me 15448
 
0.7%
of 14574
 
0.7%
no 14157
 
0.6%
Other values (236150) 1923628
86.6%

Most occurring characters

ValueCountFrequency (%)
1635323
 
13.8%
e 985846
 
8.3%
a 831482
 
7.0%
o 622983
 
5.2%
i 610106
 
5.1%
n 553982
 
4.7%
r 519226
 
4.4%
t 445938
 
3.8%
l 378543
 
3.2%
s 361331
 
3.0%
Other values (4668) 4930387
41.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7471048
62.9%
Uppercase Letter 1731546
 
14.6%
Space Separator 1635323
 
13.8%
Other Letter 297495
 
2.5%
Decimal Number 281270
 
2.4%
Other Punctuation 222317
 
1.9%
Dash Punctuation 117593
 
1.0%
Close Punctuation 47899
 
0.4%
Open Punctuation 47843
 
0.4%
Nonspacing Mark 16872
 
0.1%
Other values (12) 5941
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
י 10097
 
3.4%
ו 7873
 
2.6%
ה 6906
 
2.3%
ל 6228
 
2.1%
א 4922
 
1.7%
ר 4652
 
1.6%
4594
 
1.5%
4527
 
1.5%
ב 4203
 
1.4%
4167
 
1.4%
Other values (4110) 239326
80.4%
Lowercase Letter
ValueCountFrequency (%)
e 985846
13.2%
a 831482
11.1%
o 622983
 
8.3%
i 610106
 
8.2%
n 553982
 
7.4%
r 519226
 
6.9%
t 445938
 
6.0%
l 378543
 
5.1%
s 361331
 
4.8%
u 276252
 
3.7%
Other values (190) 1885359
25.2%
Uppercase Letter
ValueCountFrequency (%)
S 135434
 
7.8%
M 134651
 
7.8%
T 132102
 
7.6%
A 112973
 
6.5%
L 96399
 
5.6%
D 90687
 
5.2%
B 86180
 
5.0%
C 85044
 
4.9%
R 82416
 
4.8%
I 80670
 
4.7%
Other values (152) 694990
40.1%
Nonspacing Mark
ValueCountFrequency (%)
3609
21.4%
3300
19.6%
2910
17.2%
1811
10.7%
1296
 
7.7%
832
 
4.9%
686
 
4.1%
620
 
3.7%
526
 
3.1%
360
 
2.1%
Other values (27) 922
 
5.5%
Other Punctuation
ValueCountFrequency (%)
. 57370
25.8%
, 47649
21.4%
' 43395
19.5%
: 24603
11.1%
" 17199
 
7.7%
/ 12174
 
5.5%
& 5684
 
2.6%
! 5147
 
2.3%
? 4310
 
1.9%
; 1479
 
0.7%
Other values (21) 3307
 
1.5%
Other Symbol
ValueCountFrequency (%)
° 53
31.0%
33
19.3%
24
14.0%
® 12
 
7.0%
12
 
7.0%
7
 
4.1%
3
 
1.8%
3
 
1.8%
3
 
1.8%
3
 
1.8%
Other values (13) 18
 
10.5%
Math Symbol
ValueCountFrequency (%)
~ 418
41.6%
+ 273
27.2%
| 112
 
11.2%
= 63
 
6.3%
> 52
 
5.2%
< 44
 
4.4%
× 12
 
1.2%
10
 
1.0%
5
 
0.5%
5
 
0.5%
Other values (9) 10
 
1.0%
Decimal Number
ValueCountFrequency (%)
0 63921
22.7%
1 53122
18.9%
2 50631
18.0%
9 20960
 
7.5%
3 19856
 
7.1%
4 16338
 
5.8%
5 15855
 
5.6%
8 13580
 
4.8%
6 13561
 
4.8%
7 13441
 
4.8%
Other values (3) 5
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 45124
94.2%
] 2375
 
5.0%
196
 
0.4%
164
 
0.3%
17
 
< 0.1%
12
 
< 0.1%
} 5
 
< 0.1%
4
 
< 0.1%
1
 
< 0.1%
1
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 45056
94.2%
[ 2379
 
5.0%
196
 
0.4%
164
 
0.3%
17
 
< 0.1%
12
 
< 0.1%
11
 
< 0.1%
4
 
< 0.1%
{ 3
 
< 0.1%
1
 
< 0.1%
Modifier Letter
ValueCountFrequency (%)
2825
95.1%
81
 
2.7%
55
 
1.9%
4
 
0.1%
ˈ 2
 
0.1%
ˇ 1
 
< 0.1%
ـ 1
 
< 0.1%
ʻ 1
 
< 0.1%
ˋ 1
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 117055
99.5%
375
 
0.3%
115
 
0.1%
18
 
< 0.1%
16
 
< 0.1%
14
 
< 0.1%
Modifier Symbol
ValueCountFrequency (%)
´ 168
67.2%
` 77
30.8%
^ 2
 
0.8%
¨ 1
 
0.4%
΄ 1
 
0.4%
˙ 1
 
0.4%
Private Use
ValueCountFrequency (%)
4
40.0%
2
20.0%
1
 
10.0%
1
 
10.0%
1
 
10.0%
1
 
10.0%
Currency Symbol
ValueCountFrequency (%)
$ 179
92.7%
7
 
3.6%
¥ 3
 
1.6%
£ 3
 
1.6%
¢ 1
 
0.5%
Letter Number
ValueCountFrequency (%)
11
52.4%
5
23.8%
3
 
14.3%
1
 
4.8%
1
 
4.8%
Control
ValueCountFrequency (%)
† 1
20.0%
‚ 1
20.0%
’ 1
20.0%
“ 1
20.0%
„ 1
20.0%
Final Punctuation
ValueCountFrequency (%)
693
74.5%
183
 
19.7%
» 54
 
5.8%
Initial Punctuation
ValueCountFrequency (%)
162
65.3%
« 54
 
21.8%
32
 
12.9%
Format
ValueCountFrequency (%)
7
63.6%
3
27.3%
 1
 
9.1%
Space Separator
ValueCountFrequency (%)
1635323
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 127
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9097167
76.6%
Common 2357988
 
19.9%
Han 103052
 
0.9%
Cyrillic 98175
 
0.8%
Hebrew 80512
 
0.7%
Thai 77395
 
0.7%
Katakana 24826
 
0.2%
Hiragana 24662
 
0.2%
Greek 7349
 
0.1%
Arabic 2217
 
< 0.1%
Other values (7) 1804
 
< 0.1%

Most frequent character per script

Han
ValueCountFrequency (%)
2841
 
2.8%
2115
 
2.1%
1986
 
1.9%
1870
 
1.8%
1421
 
1.4%
1366
 
1.3%
1176
 
1.1%
1150
 
1.1%
949
 
0.9%
939
 
0.9%
Other values (3432) 87239
84.7%
Hangul
ValueCountFrequency (%)
32
 
2.6%
26
 
2.2%
26
 
2.2%
23
 
1.9%
22
 
1.8%
22
 
1.8%
20
 
1.7%
19
 
1.6%
19
 
1.6%
19
 
1.6%
Other values (324) 981
81.1%
Latin
ValueCountFrequency (%)
e 985846
 
10.8%
a 831482
 
9.1%
o 622983
 
6.8%
i 610106
 
6.7%
n 553982
 
6.1%
r 519226
 
5.7%
t 445938
 
4.9%
l 378543
 
4.2%
s 361331
 
4.0%
u 276252
 
3.0%
Other values (213) 3511478
38.6%
Common
ValueCountFrequency (%)
1635323
69.4%
- 117055
 
5.0%
0 63921
 
2.7%
. 57370
 
2.4%
1 53122
 
2.3%
2 50631
 
2.1%
, 47649
 
2.0%
) 45124
 
1.9%
( 45056
 
1.9%
' 43395
 
1.8%
Other values (127) 199342
 
8.5%
Katakana
ValueCountFrequency (%)
2066
 
8.3%
1257
 
5.1%
1084
 
4.4%
1056
 
4.3%
977
 
3.9%
912
 
3.7%
778
 
3.1%
675
 
2.7%
597
 
2.4%
572
 
2.3%
Other values (74) 14852
59.8%
Hiragana
ValueCountFrequency (%)
3311
 
13.4%
1715
 
7.0%
1197
 
4.9%
948
 
3.8%
895
 
3.6%
746
 
3.0%
746
 
3.0%
740
 
3.0%
656
 
2.7%
626
 
2.5%
Other values (70) 13082
53.0%
Cyrillic
ValueCountFrequency (%)
а 9712
 
9.9%
о 8175
 
8.3%
е 7528
 
7.7%
и 5574
 
5.7%
н 5527
 
5.6%
т 4935
 
5.0%
р 4330
 
4.4%
л 4227
 
4.3%
с 3976
 
4.0%
к 3307
 
3.4%
Other values (62) 40884
41.6%
Thai
ValueCountFrequency (%)
4594
 
5.9%
4527
 
5.8%
4167
 
5.4%
3661
 
4.7%
3609
 
4.7%
3608
 
4.7%
3527
 
4.6%
3300
 
4.3%
3050
 
3.9%
2910
 
3.8%
Other values (58) 40442
52.3%
Greek
ValueCountFrequency (%)
α 772
 
10.5%
ο 535
 
7.3%
ι 509
 
6.9%
τ 420
 
5.7%
ν 404
 
5.5%
ρ 319
 
4.3%
ε 313
 
4.3%
μ 291
 
4.0%
λ 285
 
3.9%
ά 270
 
3.7%
Other values (56) 3231
44.0%
Arabic
ValueCountFrequency (%)
ا 335
15.1%
ل 242
 
10.9%
ي 220
 
9.9%
ن 129
 
5.8%
م 126
 
5.7%
ب 124
 
5.6%
و 112
 
5.1%
ر 100
 
4.5%
ه 72
 
3.2%
ح 71
 
3.2%
Other values (28) 686
30.9%
Bopomofo
ValueCountFrequency (%)
6
 
12.8%
2
 
4.3%
2
 
4.3%
2
 
4.3%
2
 
4.3%
2
 
4.3%
1
 
2.1%
1
 
2.1%
1
 
2.1%
1
 
2.1%
Other values (27) 27
57.4%
Hebrew
ValueCountFrequency (%)
י 10097
12.5%
ו 7873
 
9.8%
ה 6906
 
8.6%
ל 6228
 
7.7%
א 4922
 
6.1%
ר 4652
 
5.8%
ב 4203
 
5.2%
ת 3934
 
4.9%
ש 3735
 
4.6%
מ 3684
 
4.6%
Other values (20) 24278
30.2%
Lao
ValueCountFrequency (%)
3
 
7.1%
3
 
7.1%
2
 
4.8%
2
 
4.8%
2
 
4.8%
2
 
4.8%
2
 
4.8%
2
 
4.8%
2
 
4.8%
2
 
4.8%
Other values (18) 20
47.6%
Inherited
ValueCountFrequency (%)
205
44.0%
́ 101
21.7%
45
 
9.7%
̈ 30
 
6.4%
̃ 27
 
5.8%
̆ 17
 
3.6%
̊ 15
 
3.2%
̧ 11
 
2.4%
̂ 10
 
2.1%
̀ 3
 
0.6%
Other values (2) 2
 
0.4%
Tibetan
ValueCountFrequency (%)
1
8.3%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
Other values (2) 2
16.7%
Georgian
ValueCountFrequency (%)
6
33.3%
2
 
11.1%
2
 
11.1%
2
 
11.1%
2
 
11.1%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
Unknown
ValueCountFrequency (%)
4
40.0%
2
20.0%
1
 
10.0%
1
 
10.0%
1
 
10.0%
1
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11320559
95.3%
None 136036
 
1.1%
CJK 102995
 
0.9%
Cyrillic 98175
 
0.8%
Hebrew 80512
 
0.7%
Thai 77395
 
0.7%
Katakana 29058
 
0.2%
Hiragana 24912
 
0.2%
Arabic 2218
 
< 0.1%
Punctuation 1515
 
< 0.1%
Other values (24) 1772
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1635323
 
14.4%
e 985846
 
8.7%
a 831482
 
7.3%
o 622983
 
5.5%
i 610106
 
5.4%
n 553982
 
4.9%
r 519226
 
4.6%
t 445938
 
3.9%
l 378543
 
3.3%
s 361331
 
3.2%
Other values (85) 4375799
38.7%
None
ValueCountFrequency (%)
é 11484
 
8.4%
ä 11361
 
8.4%
á 10970
 
8.1%
í 8657
 
6.4%
ó 8354
 
6.1%
ö 7107
 
5.2%
ı 6723
 
4.9%
ü 6314
 
4.6%
å 4343
 
3.2%
ñ 3452
 
2.5%
Other values (243) 57271
42.1%
Hebrew
ValueCountFrequency (%)
י 10097
12.5%
ו 7873
 
9.8%
ה 6906
 
8.6%
ל 6228
 
7.7%
א 4922
 
6.1%
ר 4652
 
5.8%
ב 4203
 
5.2%
ת 3934
 
4.9%
ש 3735
 
4.6%
מ 3684
 
4.6%
Other values (20) 24278
30.2%
Cyrillic
ValueCountFrequency (%)
а 9712
 
9.9%
о 8175
 
8.3%
е 7528
 
7.7%
и 5574
 
5.7%
н 5527
 
5.6%
т 4935
 
5.0%
р 4330
 
4.4%
л 4227
 
4.3%
с 3976
 
4.0%
к 3307
 
3.4%
Other values (62) 40884
41.6%
Thai
ValueCountFrequency (%)
4594
 
5.9%
4527
 
5.8%
4167
 
5.4%
3661
 
4.7%
3609
 
4.7%
3608
 
4.7%
3527
 
4.6%
3300
 
4.3%
3050
 
3.9%
2910
 
3.8%
Other values (58) 40442
52.3%
Hiragana
ValueCountFrequency (%)
3311
 
13.3%
1715
 
6.9%
1197
 
4.8%
948
 
3.8%
895
 
3.6%
746
 
3.0%
746
 
3.0%
740
 
3.0%
656
 
2.6%
626
 
2.5%
Other values (72) 13332
53.5%
CJK
ValueCountFrequency (%)
2841
 
2.8%
2115
 
2.1%
1986
 
1.9%
1870
 
1.8%
1421
 
1.4%
1366
 
1.3%
1176
 
1.1%
1150
 
1.1%
949
 
0.9%
939
 
0.9%
Other values (3430) 87182
84.6%
Katakana
ValueCountFrequency (%)
2825
 
9.7%
2066
 
7.1%
1407
 
4.8%
1257
 
4.3%
1084
 
3.7%
1056
 
3.6%
977
 
3.4%
912
 
3.1%
778
 
2.7%
675
 
2.3%
Other values (76) 16021
55.1%
Punctuation
ValueCountFrequency (%)
693
45.7%
247
 
16.3%
183
 
12.1%
162
 
10.7%
115
 
7.6%
32
 
2.1%
18
 
1.2%
16
 
1.1%
14
 
0.9%
11
 
0.7%
Other values (6) 24
 
1.6%
Arabic
ValueCountFrequency (%)
ا 335
15.1%
ل 242
 
10.9%
ي 220
 
9.9%
ن 129
 
5.8%
م 126
 
5.7%
ب 124
 
5.6%
و 112
 
5.0%
ر 100
 
4.5%
ه 72
 
3.2%
ح 71
 
3.2%
Other values (29) 687
31.0%
Diacriticals
ValueCountFrequency (%)
́ 101
47.0%
̈ 30
 
14.0%
̃ 27
 
12.6%
̆ 17
 
7.9%
̊ 15
 
7.0%
̧ 11
 
5.1%
̂ 10
 
4.7%
̀ 3
 
1.4%
̦ 1
 
0.5%
Misc Symbols
ValueCountFrequency (%)
33
44.6%
24
32.4%
7
 
9.5%
2
 
2.7%
2
 
2.7%
1
 
1.4%
1
 
1.4%
1
 
1.4%
1
 
1.4%
1
 
1.4%
Hangul
ValueCountFrequency (%)
32
 
2.8%
26
 
2.3%
26
 
2.3%
23
 
2.0%
22
 
1.9%
22
 
1.9%
20
 
1.7%
19
 
1.7%
19
 
1.7%
19
 
1.7%
Other values (301) 920
80.1%
Small Forms
ValueCountFrequency (%)
14
100.0%
Letterlike Symbols
ValueCountFrequency (%)
12
100.0%
Number Forms
ValueCountFrequency (%)
11
52.4%
5
23.8%
3
 
14.3%
1
 
4.8%
1
 
4.8%
Arrows
ValueCountFrequency (%)
10
66.7%
5
33.3%
IPA Ext
ValueCountFrequency (%)
ə 7
77.8%
ɪ 2
 
22.2%
Jamo
ValueCountFrequency (%)
7
 
11.5%
7
 
11.5%
6
 
9.8%
4
 
6.6%
4
 
6.6%
3
 
4.9%
3
 
4.9%
3
 
4.9%
3
 
4.9%
3
 
4.9%
Other values (13) 18
29.5%
Currency Symbols
ValueCountFrequency (%)
7
100.0%
Latin Ext Additional
ValueCountFrequency (%)
ế 7
25.9%
3
11.1%
2
 
7.4%
2
 
7.4%
2
 
7.4%
2
 
7.4%
1
 
3.7%
1
 
3.7%
1
 
3.7%
1
 
3.7%
Other values (5) 5
18.5%
Bopomofo
ValueCountFrequency (%)
6
 
12.8%
2
 
4.3%
2
 
4.3%
2
 
4.3%
2
 
4.3%
2
 
4.3%
1
 
2.1%
1
 
2.1%
1
 
2.1%
1
 
2.1%
Other values (27) 27
57.4%
Georgian
ValueCountFrequency (%)
6
33.3%
2
 
11.1%
2
 
11.1%
2
 
11.1%
2
 
11.1%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
Math Operators
ValueCountFrequency (%)
5
50.0%
1
 
10.0%
1
 
10.0%
1
 
10.0%
1
 
10.0%
1
 
10.0%
PUA
ValueCountFrequency (%)
4
40.0%
2
20.0%
1
 
10.0%
1
 
10.0%
1
 
10.0%
1
 
10.0%
Lao
ValueCountFrequency (%)
3
 
7.1%
3
 
7.1%
2
 
4.8%
2
 
4.8%
2
 
4.8%
2
 
4.8%
2
 
4.8%
2
 
4.8%
2
 
4.8%
2
 
4.8%
Other values (18) 20
47.6%
Geometric Shapes
ValueCountFrequency (%)
3
21.4%
3
21.4%
3
21.4%
3
21.4%
1
 
7.1%
1
 
7.1%
Specials
ValueCountFrequency (%)
3
100.0%
Modifier Letters
ValueCountFrequency (%)
ˈ 2
33.3%
ˇ 1
16.7%
ʻ 1
16.7%
ˋ 1
16.7%
˙ 1
16.7%
CJK Ext B
ValueCountFrequency (%)
𠱁 2
100.0%
Tibetan
ValueCountFrequency (%)
1
8.3%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
Other values (2) 2
16.7%
Sup Math Operators
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%
Dingbats
ValueCountFrequency (%)
1
100.0%
VS
ValueCountFrequency (%)
1
100.0%

popularity
Real number (ℝ)

Distinct101
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.570053
Minimum0
Maximum100
Zeros44690
Zeros (%)7.6%
Negative0
Negative (%)0.0%
Memory size4.5 MiB
2023-01-27T23:30:26.640136image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q113
median27
Q341
95-th percentile59
Maximum100
Range100
Interquartile range (IQR)28

Descriptive statistics

Standard deviation18.370642
Coefficient of variation (CV)0.66632598
Kurtosis-0.63280211
Mean27.570053
Median Absolute Deviation (MAD)14
Skewness0.278697
Sum16174578
Variance337.4805
MonotonicityDecreasing
2023-01-27T23:30:26.766705image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 44690
 
7.6%
35 12231
 
2.1%
23 12139
 
2.1%
1 12024
 
2.0%
36 11879
 
2.0%
34 11328
 
1.9%
27 11292
 
1.9%
22 11206
 
1.9%
33 11174
 
1.9%
24 11148
 
1.9%
Other values (91) 437561
74.6%
ValueCountFrequency (%)
0 44690
7.6%
1 12024
 
2.0%
2 9639
 
1.6%
3 8154
 
1.4%
4 7733
 
1.3%
5 7730
 
1.3%
6 7659
 
1.3%
7 7726
 
1.3%
8 7988
 
1.4%
9 8265
 
1.4%
ValueCountFrequency (%)
100 1
 
< 0.1%
99 1
 
< 0.1%
98 1
 
< 0.1%
97 2
 
< 0.1%
96 2
 
< 0.1%
95 1
 
< 0.1%
94 6
< 0.1%
93 2
 
< 0.1%
92 10
< 0.1%
91 11
< 0.1%

duration_ms
Real number (ℝ)

Distinct123122
Distinct (%)21.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean230051.17
Minimum3344
Maximum5621218
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 MiB
2023-01-27T23:30:26.885749image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum3344
5-th percentile97307
Q1175093
median214893
Q3263867
95-th percentile382333
Maximum5621218
Range5617874
Interquartile range (IQR)88774

Descriptive statistics

Standard deviation126526.09
Coefficient of variation (CV)0.54999107
Kurtosis241.06655
Mean230051.17
Median Absolute Deviation (MAD)43838.5
Skewness10.325622
Sum1.3496458 × 1011
Variance1.6008851 × 1010
MonotonicityNot monotonic
2023-01-27T23:30:26.994373image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
240000 215
 
< 0.1%
192000 201
 
< 0.1%
180000 199
 
< 0.1%
216000 184
 
< 0.1%
210000 171
 
< 0.1%
200000 166
 
< 0.1%
184000 166
 
< 0.1%
208000 162
 
< 0.1%
228000 152
 
< 0.1%
198000 151
 
< 0.1%
Other values (123112) 584905
99.7%
ValueCountFrequency (%)
3344 4
< 0.1%
4000 8
< 0.1%
4937 1
 
< 0.1%
5108 1
 
< 0.1%
5991 1
 
< 0.1%
6360 1
 
< 0.1%
6362 1
 
< 0.1%
6373 3
 
< 0.1%
7523 1
 
< 0.1%
8594 1
 
< 0.1%
ValueCountFrequency (%)
5621218 1
< 0.1%
5403500 1
< 0.1%
5042185 1
< 0.1%
4995083 1
< 0.1%
4864333 1
< 0.1%
4800118 1
< 0.1%
4797258 1
< 0.1%
4792587 1
< 0.1%
4786672 1
< 0.1%
4775518 1
< 0.1%

explicit
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.5 MiB
0
560808 
1
 
25864

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters586672
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 560808
95.6%
1 25864
 
4.4%

Length

2023-01-27T23:30:27.111652image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-27T23:30:27.201137image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 560808
95.6%
1 25864
 
4.4%

Most occurring characters

ValueCountFrequency (%)
0 560808
95.6%
1 25864
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 586672
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 560808
95.6%
1 25864
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
Common 586672
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 560808
95.6%
1 25864
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 586672
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 560808
95.6%
1 25864
 
4.4%

artists
Categorical

Distinct114030
Distinct (%)19.4%
Missing0
Missing (%)0.0%
Memory size4.5 MiB
['Die drei ???']
 
3856
['TKKG Retro-Archiv']
 
2006
['Benjamin Blümchen']
 
1503
['Bibi Blocksberg']
 
1472
['Lata Mangeshkar']
 
1373
Other values (114025)
576462 

Length

Max length934
Median length492
Mean length21.612956
Min length4

Characters and Unicode

Total characters12679716
Distinct characters2156
Distinct categories20 ?
Distinct scripts13 ?
Distinct blocks22 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique66232 ?
Unique (%)11.3%

Sample

1st row['Justin Bieber', 'Daniel Caesar', 'Giveon']
2nd row['Olivia Rodrigo']
3rd row['Masked Wolf']
4th row['The Weeknd']
5th row['Kali Uchis']

Common Values

ValueCountFrequency (%)
['Die drei ???'] 3856
 
0.7%
['TKKG Retro-Archiv'] 2006
 
0.3%
['Benjamin Blümchen'] 1503
 
0.3%
['Bibi Blocksberg'] 1472
 
0.3%
['Lata Mangeshkar'] 1373
 
0.2%
['Bibi und Tina'] 927
 
0.2%
['Tintin', 'Tomas Bolme', 'Bert-Åke Varg'] 905
 
0.2%
['Francisco Canaro'] 891
 
0.2%
['Ella Fitzgerald'] 870
 
0.1%
['Tadeusz Dolega Mostowicz'] 838
 
0.1%
Other values (114020) 572031
97.5%

Length

2023-01-27T23:30:27.297317image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the 29997
 
1.9%
22820
 
1.5%
orchestra 12441
 
0.8%
de 10079
 
0.6%
los 9267
 
0.6%
die 5267
 
0.3%
la 4812
 
0.3%
del 4260
 
0.3%
john 4178
 
0.3%
his 4157
 
0.3%
Other values (80592) 1451219
93.1%

Most occurring characters

ValueCountFrequency (%)
' 1507306
 
11.9%
971828
 
7.7%
a 885658
 
7.0%
e 763743
 
6.0%
i 607358
 
4.8%
[ 586740
 
4.6%
] 586740
 
4.6%
r 583100
 
4.6%
n 578674
 
4.6%
o 557799
 
4.4%
Other values (2146) 5050770
39.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7049614
55.6%
Other Punctuation 1762841
 
13.9%
Uppercase Letter 1618636
 
12.8%
Space Separator 971828
 
7.7%
Close Punctuation 587847
 
4.6%
Open Punctuation 587845
 
4.6%
Other Letter 58839
 
0.5%
Decimal Number 19835
 
0.2%
Dash Punctuation 14763
 
0.1%
Nonspacing Mark 5568
 
< 0.1%
Other values (10) 2100
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1888
 
3.2%
1619
 
2.8%
1187
 
2.0%
1120
 
1.9%
1083
 
1.8%
955
 
1.6%
917
 
1.6%
915
 
1.6%
736
 
1.3%
731
 
1.2%
Other values (1740) 47688
81.0%
Lowercase Letter
ValueCountFrequency (%)
a 885658
12.6%
e 763743
10.8%
i 607358
 
8.6%
r 583100
 
8.3%
n 578674
 
8.2%
o 557799
 
7.9%
l 397378
 
5.6%
s 395691
 
5.6%
t 328892
 
4.7%
h 256752
 
3.6%
Other values (169) 1694569
24.0%
Uppercase Letter
ValueCountFrequency (%)
S 138185
 
8.5%
M 122570
 
7.6%
B 114524
 
7.1%
C 102008
 
6.3%
A 100915
 
6.2%
T 98618
 
6.1%
D 83448
 
5.2%
L 82798
 
5.1%
R 79486
 
4.9%
P 76285
 
4.7%
Other values (116) 619799
38.3%
Other Punctuation
ValueCountFrequency (%)
' 1507306
85.5%
, 173449
 
9.8%
. 31571
 
1.8%
& 17636
 
1.0%
" 17113
 
1.0%
? 11659
 
0.7%
/ 1513
 
0.1%
! 1455
 
0.1%
: 250
 
< 0.1%
233
 
< 0.1%
Other values (16) 656
 
< 0.1%
Nonspacing Mark
ValueCountFrequency (%)
1404
25.2%
1062
19.1%
769
13.8%
573
10.3%
458
 
8.2%
391
 
7.0%
280
 
5.0%
262
 
4.7%
141
 
2.5%
98
 
1.8%
Other values (4) 130
 
2.3%
Decimal Number
ValueCountFrequency (%)
1 3345
16.9%
2 3192
16.1%
0 2836
14.3%
4 1944
9.8%
3 1825
9.2%
5 1705
8.6%
7 1357
6.8%
9 1310
 
6.6%
8 1211
 
6.1%
6 1110
 
5.6%
Math Symbol
ValueCountFrequency (%)
+ 186
78.8%
| 25
 
10.6%
= 12
 
5.1%
× 3
 
1.3%
~ 3
 
1.3%
> 2
 
0.8%
2
 
0.8%
1
 
0.4%
< 1
 
0.4%
1
 
0.4%
Modifier Symbol
ValueCountFrequency (%)
´ 69
69.7%
` 21
 
21.2%
^ 4
 
4.0%
¨ 3
 
3.0%
¯ 1
 
1.0%
1
 
1.0%
Other Symbol
ValueCountFrequency (%)
21
48.8%
° 10
23.3%
5
 
11.6%
® 4
 
9.3%
2
 
4.7%
© 1
 
2.3%
Currency Symbol
ValueCountFrequency (%)
$ 456
96.6%
¥ 11
 
2.3%
3
 
0.6%
¢ 1
 
0.2%
£ 1
 
0.2%
Open Punctuation
ValueCountFrequency (%)
[ 586740
99.8%
( 1087
 
0.2%
10
 
< 0.1%
8
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
] 586740
99.8%
) 1090
 
0.2%
10
 
< 0.1%
7
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 14649
99.2%
110
 
0.7%
4
 
< 0.1%
Final Punctuation
ValueCountFrequency (%)
156
63.7%
» 56
 
22.9%
33
 
13.5%
Initial Punctuation
ValueCountFrequency (%)
« 55
80.9%
10
 
14.7%
3
 
4.4%
Modifier Letter
ValueCountFrequency (%)
869
99.2%
7
 
0.8%
Other Number
ValueCountFrequency (%)
² 3
75.0%
³ 1
 
25.0%
Space Separator
ValueCountFrequency (%)
971828
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 56
100.0%
Letter Number
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8626881
68.0%
Common 3947045
31.1%
Cyrillic 35174
 
0.3%
Han 30733
 
0.2%
Thai 24963
 
0.2%
Greek 6209
 
< 0.1%
Katakana 5238
 
< 0.1%
Hebrew 1830
 
< 0.1%
Hiragana 1050
 
< 0.1%
Arabic 467
 
< 0.1%
Other values (3) 126
 
< 0.1%

Most frequent character per script

Han
ValueCountFrequency (%)
731
 
2.4%
505
 
1.6%
403
 
1.3%
396
 
1.3%
392
 
1.3%
387
 
1.3%
376
 
1.2%
357
 
1.2%
325
 
1.1%
314
 
1.0%
Other values (1430) 26547
86.4%
Latin
ValueCountFrequency (%)
a 885658
 
10.3%
e 763743
 
8.9%
i 607358
 
7.0%
r 583100
 
6.8%
n 578674
 
6.7%
o 557799
 
6.5%
l 397378
 
4.6%
s 395691
 
4.6%
t 328892
 
3.8%
h 256752
 
3.0%
Other values (166) 3271836
37.9%
Common
ValueCountFrequency (%)
' 1507306
38.2%
971828
24.6%
[ 586740
 
14.9%
] 586740
 
14.9%
, 173449
 
4.4%
. 31571
 
0.8%
& 17636
 
0.4%
" 17113
 
0.4%
- 14649
 
0.4%
? 11659
 
0.3%
Other values (75) 28354
 
0.7%
Katakana
ValueCountFrequency (%)
484
 
9.2%
447
 
8.5%
422
 
8.1%
385
 
7.4%
341
 
6.5%
334
 
6.4%
332
 
6.3%
306
 
5.8%
127
 
2.4%
104
 
2.0%
Other values (70) 1956
37.3%
Cyrillic
ValueCountFrequency (%)
а 3565
 
10.1%
и 2858
 
8.1%
о 2645
 
7.5%
н 2624
 
7.5%
е 2133
 
6.1%
р 2083
 
5.9%
в 1795
 
5.1%
л 1487
 
4.2%
с 1284
 
3.7%
т 1263
 
3.6%
Other values (58) 13437
38.2%
Hiragana
ValueCountFrequency (%)
62
 
5.9%
61
 
5.8%
58
 
5.5%
58
 
5.5%
57
 
5.4%
39
 
3.7%
39
 
3.7%
35
 
3.3%
33
 
3.1%
32
 
3.0%
Other values (54) 576
54.9%
Thai
ValueCountFrequency (%)
1888
 
7.6%
1619
 
6.5%
1404
 
5.6%
1187
 
4.8%
1120
 
4.5%
1083
 
4.3%
1062
 
4.3%
955
 
3.8%
917
 
3.7%
915
 
3.7%
Other values (52) 12813
51.3%
Hangul
ValueCountFrequency (%)
8
 
7.3%
6
 
5.5%
5
 
4.6%
3
 
2.8%
3
 
2.8%
3
 
2.8%
3
 
2.8%
3
 
2.8%
3
 
2.8%
3
 
2.8%
Other values (51) 69
63.3%
Greek
ValueCountFrequency (%)
ς 562
 
9.1%
α 514
 
8.3%
ο 418
 
6.7%
ρ 362
 
5.8%
τ 361
 
5.8%
η 310
 
5.0%
ν 250
 
4.0%
ι 233
 
3.8%
λ 213
 
3.4%
κ 213
 
3.4%
Other values (44) 2773
44.7%
Hebrew
ValueCountFrequency (%)
י 314
17.2%
ו 195
10.7%
ר 144
 
7.9%
ה 123
 
6.7%
ב 112
 
6.1%
א 107
 
5.8%
נ 107
 
5.8%
ל 104
 
5.7%
ן 82
 
4.5%
מ 57
 
3.1%
Other values (18) 485
26.5%
Arabic
ValueCountFrequency (%)
م 51
10.9%
ي 49
 
10.5%
ا 37
 
7.9%
د 34
 
7.3%
ل 34
 
7.3%
ز 33
 
7.1%
ف 31
 
6.6%
ر 26
 
5.6%
ح 25
 
5.4%
و 22
 
4.7%
Other values (18) 125
26.8%
Georgian
ValueCountFrequency (%)
4
28.6%
3
21.4%
1
 
7.1%
1
 
7.1%
1
 
7.1%
1
 
7.1%
1
 
7.1%
1
 
7.1%
1
 
7.1%
Inherited
ValueCountFrequency (%)
́ 3
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12482277
98.4%
None 96402
 
0.8%
Cyrillic 35174
 
0.3%
CJK 30717
 
0.2%
Thai 24963
 
0.2%
Katakana 6340
 
0.1%
Hebrew 1830
 
< 0.1%
Hiragana 1051
 
< 0.1%
Arabic 467
 
< 0.1%
Punctuation 316
 
< 0.1%
Other values (12) 179
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
' 1507306
 
12.1%
971828
 
7.8%
a 885658
 
7.1%
e 763743
 
6.1%
i 607358
 
4.9%
[ 586740
 
4.7%
] 586740
 
4.7%
r 583100
 
4.7%
n 578674
 
4.6%
o 557799
 
4.5%
Other values (83) 4853331
38.9%
None
ValueCountFrequency (%)
é 14064
 
14.6%
á 9701
 
10.1%
ü 7827
 
8.1%
ó 6875
 
7.1%
í 6494
 
6.7%
ö 5330
 
5.5%
ı 2911
 
3.0%
ä 2518
 
2.6%
ç 2191
 
2.3%
ú 2179
 
2.3%
Other values (192) 36312
37.7%
Cyrillic
ValueCountFrequency (%)
а 3565
 
10.1%
и 2858
 
8.1%
о 2645
 
7.5%
н 2624
 
7.5%
е 2133
 
6.1%
р 2083
 
5.9%
в 1795
 
5.1%
л 1487
 
4.2%
с 1284
 
3.7%
т 1263
 
3.6%
Other values (58) 13437
38.2%
Thai
ValueCountFrequency (%)
1888
 
7.6%
1619
 
6.5%
1404
 
5.6%
1187
 
4.8%
1120
 
4.5%
1083
 
4.3%
1062
 
4.3%
955
 
3.8%
917
 
3.7%
915
 
3.7%
Other values (52) 12813
51.3%
Katakana
ValueCountFrequency (%)
869
 
13.7%
484
 
7.6%
447
 
7.1%
422
 
6.7%
385
 
6.1%
341
 
5.4%
334
 
5.3%
332
 
5.2%
306
 
4.8%
233
 
3.7%
Other values (72) 2187
34.5%
CJK
ValueCountFrequency (%)
731
 
2.4%
505
 
1.6%
403
 
1.3%
396
 
1.3%
392
 
1.3%
387
 
1.3%
376
 
1.2%
357
 
1.2%
325
 
1.1%
314
 
1.0%
Other values (1427) 26531
86.4%
Hebrew
ValueCountFrequency (%)
י 314
17.2%
ו 195
10.7%
ר 144
 
7.9%
ה 123
 
6.7%
ב 112
 
6.1%
א 107
 
5.8%
נ 107
 
5.8%
ל 104
 
5.7%
ן 82
 
4.5%
מ 57
 
3.1%
Other values (18) 485
26.5%
Punctuation
ValueCountFrequency (%)
156
49.4%
110
34.8%
33
 
10.4%
10
 
3.2%
3
 
0.9%
3
 
0.9%
1
 
0.3%
Hiragana
ValueCountFrequency (%)
62
 
5.9%
61
 
5.8%
58
 
5.5%
58
 
5.5%
57
 
5.4%
39
 
3.7%
39
 
3.7%
35
 
3.3%
33
 
3.1%
32
 
3.0%
Other values (55) 577
54.9%
Arabic
ValueCountFrequency (%)
م 51
10.9%
ي 49
 
10.5%
ا 37
 
7.9%
د 34
 
7.3%
ل 34
 
7.3%
ز 33
 
7.1%
ف 31
 
6.6%
ر 26
 
5.6%
ح 25
 
5.4%
و 22
 
4.7%
Other values (18) 125
26.8%
Misc Symbols
ValueCountFrequency (%)
21
91.3%
2
 
8.7%
Hangul
ValueCountFrequency (%)
8
 
7.3%
6
 
5.5%
5
 
4.6%
3
 
2.8%
3
 
2.8%
3
 
2.8%
3
 
2.8%
3
 
2.8%
3
 
2.8%
3
 
2.8%
Other values (51) 69
63.3%
CJK Compat Ideographs
ValueCountFrequency (%)
7
100.0%
Latin Ext Additional
ValueCountFrequency (%)
6
75.0%
1
 
12.5%
1
 
12.5%
Letterlike Symbols
ValueCountFrequency (%)
5
100.0%
Georgian
ValueCountFrequency (%)
4
28.6%
3
21.4%
1
 
7.1%
1
 
7.1%
1
 
7.1%
1
 
7.1%
1
 
7.1%
1
 
7.1%
1
 
7.1%
Currency Symbols
ValueCountFrequency (%)
3
100.0%
Diacriticals
ValueCountFrequency (%)
́ 3
100.0%
CJK Ext B
ValueCountFrequency (%)
𤒹 2
100.0%
Math Operators
ValueCountFrequency (%)
2
66.7%
1
33.3%
Number Forms
ValueCountFrequency (%)
1
100.0%
Arrows
ValueCountFrequency (%)
1
100.0%

id_artists
Categorical

Distinct115062
Distinct (%)19.6%
Missing0
Missing (%)0.0%
Memory size4.5 MiB
['3meJIgRw7YleJrmbpbJK6S']
 
3856
['0i38tQX5j4gZ0KS3eCMoIl']
 
2006
['1l6d0RIxTL3JytlLGvWzYe']
 
1503
['3t2iKODSDyzoDJw7AsD99u']
 
1472
['61JrslREXq98hurYL2hYoc']
 
1373
Other values (115057)
576462 

Length

Max length1508
Median length26
Mean length33.556093
Min length26

Characters and Unicode

Total characters19686420
Distinct characters67
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique67145 ?
Unique (%)11.4%

Sample

1st row['1uNFoZAHBGtllmzznpCI3s', '20wkVLutqVOYrc0kxFs7rA', '4fxd5Ee7UefO4CUXgwJ7IP']
2nd row['1McMsnEElThX1knmY4oliG']
3rd row['1uU7g3DNSbsu0QjSEqZtEd']
4th row['1Xyo4u8uXC1ZmMpatF05PJ']
5th row['1U1el3k54VvEUzo3ybLPlM']

Common Values

ValueCountFrequency (%)
['3meJIgRw7YleJrmbpbJK6S'] 3856
 
0.7%
['0i38tQX5j4gZ0KS3eCMoIl'] 2006
 
0.3%
['1l6d0RIxTL3JytlLGvWzYe'] 1503
 
0.3%
['3t2iKODSDyzoDJw7AsD99u'] 1472
 
0.3%
['61JrslREXq98hurYL2hYoc'] 1373
 
0.2%
['2x8vG4f0HYXzMEo3xNsoiI'] 927
 
0.2%
['6aMD1KAa5i3Myy61cR8FiW', '7HjbJ8V87zrxkSzL1KieQk', '71ADe4Zg9UyE8WQEHbJSXM'] 905
 
0.2%
['2maQMqxNnlRrBrS1oAsrX9'] 891
 
0.2%
['5V0MlUE1Bft0mbLlND7FJz'] 870
 
0.1%
['4eeMulNeqpZGBxybCxZOdC'] 838
 
0.1%
Other values (115052) 572031
97.5%

Length

2023-01-27T23:30:27.420404image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3mejigrw7ylejrmbpbjk6s 3856
 
0.5%
61jrslrexq98huryl2hyoc 2605
 
0.3%
5aiqb5nvvvmfsvsdexz408 2020
 
0.3%
2maqmqxnnlrrbrs1oasrx9 2010
 
0.3%
0i38tqx5j4gz0ks3ecmoil 2006
 
0.3%
4njhfmfw43rlbljqvxdurs 1821
 
0.2%
0gxdpqwyndodn7fb0rdn8j 1553
 
0.2%
1l6d0rixtl3jytllgvwzye 1503
 
0.2%
3t2ikodsdyzodjw7asd99u 1472
 
0.2%
2woqmjp9tyabvthdosotus 1253
 
0.2%
Other values (98494) 737071
97.3%

Most occurring characters

ValueCountFrequency (%)
' 1514340
 
7.7%
[ 586672
 
3.0%
] 586672
 
3.0%
0 365662
 
1.9%
4 359536
 
1.8%
2 358258
 
1.8%
5 358255
 
1.8%
3 353126
 
1.8%
1 350394
 
1.8%
6 344241
 
1.7%
Other values (57) 14509264
73.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6705473
34.1%
Uppercase Letter 6611755
33.6%
Decimal Number 3340512
17.0%
Other Punctuation 1684838
 
8.6%
Open Punctuation 586672
 
3.0%
Close Punctuation 586672
 
3.0%
Space Separator 170498
 
0.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 275758
 
4.1%
l 267655
 
4.0%
m 266604
 
4.0%
b 264203
 
3.9%
y 263309
 
3.9%
x 263030
 
3.9%
r 262848
 
3.9%
s 262422
 
3.9%
q 261029
 
3.9%
o 260528
 
3.9%
Other values (16) 4058087
60.5%
Uppercase Letter
ValueCountFrequency (%)
S 267989
 
4.1%
D 267159
 
4.0%
J 266786
 
4.0%
C 261777
 
4.0%
X 261442
 
4.0%
R 259970
 
3.9%
Y 259593
 
3.9%
O 257776
 
3.9%
L 254702
 
3.9%
I 254611
 
3.9%
Other values (16) 3999950
60.5%
Decimal Number
ValueCountFrequency (%)
0 365662
10.9%
4 359536
10.8%
2 358258
10.7%
5 358255
10.7%
3 353126
10.6%
1 350394
10.5%
6 344241
10.3%
7 335002
10.0%
8 261694
7.8%
9 254344
7.6%
Other Punctuation
ValueCountFrequency (%)
' 1514340
89.9%
, 170498
 
10.1%
Open Punctuation
ValueCountFrequency (%)
[ 586672
100.0%
Close Punctuation
ValueCountFrequency (%)
] 586672
100.0%
Space Separator
ValueCountFrequency (%)
170498
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 13317228
67.6%
Common 6369192
32.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 275758
 
2.1%
S 267989
 
2.0%
l 267655
 
2.0%
D 267159
 
2.0%
J 266786
 
2.0%
m 266604
 
2.0%
b 264203
 
2.0%
y 263309
 
2.0%
x 263030
 
2.0%
r 262848
 
2.0%
Other values (42) 10651887
80.0%
Common
ValueCountFrequency (%)
' 1514340
23.8%
[ 586672
 
9.2%
] 586672
 
9.2%
0 365662
 
5.7%
4 359536
 
5.6%
2 358258
 
5.6%
5 358255
 
5.6%
3 353126
 
5.5%
1 350394
 
5.5%
6 344241
 
5.4%
Other values (5) 1192036
18.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19686420
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
' 1514340
 
7.7%
[ 586672
 
3.0%
] 586672
 
3.0%
0 365662
 
1.9%
4 359536
 
1.8%
2 358258
 
1.8%
5 358255
 
1.8%
3 353126
 
1.8%
1 350394
 
1.8%
6 344241
 
1.7%
Other values (57) 14509264
73.7%

release_date
Categorical

Distinct19700
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size4.5 MiB
1998-01-01
 
2893
1997-01-01
 
2892
1995
 
2871
1997
 
2811
1996
 
2776
Other values (19695)
572429 

Length

Max length10
Median length10
Mean length8.5933537
Min length4

Characters and Unicode

Total characters5041480
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2176 ?
Unique (%)0.4%

Sample

1st row2021-03-19
2nd row2021-01-08
3rd row2021-01-06
4th row2020-03-20
5th row2020-12-04

Common Values

ValueCountFrequency (%)
1998-01-01 2893
 
0.5%
1997-01-01 2892
 
0.5%
1995 2871
 
0.5%
1997 2811
 
0.5%
1996 2776
 
0.5%
1990-01-01 2752
 
0.5%
1998 2726
 
0.5%
1996-01-01 2705
 
0.5%
1994 2611
 
0.4%
1995-01-01 2575
 
0.4%
Other values (19690) 559060
95.3%

Length

2023-01-27T23:30:27.520986image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1998-01-01 2893
 
0.5%
1997-01-01 2892
 
0.5%
1995 2871
 
0.5%
1997 2811
 
0.5%
1996 2776
 
0.5%
1990-01-01 2752
 
0.5%
1998 2726
 
0.5%
1996-01-01 2705
 
0.5%
1994 2611
 
0.4%
1995-01-01 2575
 
0.4%
Other values (19690) 559060
95.3%

Most occurring characters

ValueCountFrequency (%)
1 1100398
21.8%
0 1000347
19.8%
- 898264
17.8%
9 604970
12.0%
2 479776
9.5%
8 194866
 
3.9%
7 173992
 
3.5%
6 162608
 
3.2%
5 153465
 
3.0%
3 143262
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4143216
82.2%
Dash Punctuation 898264
 
17.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1100398
26.6%
0 1000347
24.1%
9 604970
14.6%
2 479776
11.6%
8 194866
 
4.7%
7 173992
 
4.2%
6 162608
 
3.9%
5 153465
 
3.7%
3 143262
 
3.5%
4 129532
 
3.1%
Dash Punctuation
ValueCountFrequency (%)
- 898264
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5041480
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1100398
21.8%
0 1000347
19.8%
- 898264
17.8%
9 604970
12.0%
2 479776
9.5%
8 194866
 
3.9%
7 173992
 
3.5%
6 162608
 
3.2%
5 153465
 
3.0%
3 143262
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5041480
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1100398
21.8%
0 1000347
19.8%
- 898264
17.8%
9 604970
12.0%
2 479776
9.5%
8 194866
 
3.9%
7 173992
 
3.5%
6 162608
 
3.2%
5 153465
 
3.0%
3 143262
 
2.8%

danceability
Real number (ℝ)

Distinct1285
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.56359382
Minimum0
Maximum0.991
Zeros328
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size4.5 MiB
2023-01-27T23:30:27.629521image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.267
Q10.453
median0.577
Q30.686
95-th percentile0.815
Maximum0.991
Range0.991
Interquartile range (IQR)0.233

Descriptive statistics

Standard deviation0.16610265
Coefficient of variation (CV)0.2947205
Kurtosis-0.27402096
Mean0.56359382
Median Absolute Deviation (MAD)0.115
Skewness-0.33082544
Sum330644.71
Variance0.027590092
MonotonicityNot monotonic
2023-01-27T23:30:27.828567image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.637 1483
 
0.3%
0.629 1453
 
0.2%
0.602 1446
 
0.2%
0.595 1444
 
0.2%
0.616 1442
 
0.2%
0.63 1440
 
0.2%
0.62 1437
 
0.2%
0.632 1433
 
0.2%
0.607 1431
 
0.2%
0.565 1429
 
0.2%
Other values (1275) 572234
97.5%
ValueCountFrequency (%)
0 328
0.1%
0.0532 1
 
< 0.1%
0.0546 1
 
< 0.1%
0.0559 2
 
< 0.1%
0.0562 1
 
< 0.1%
0.0569 2
 
< 0.1%
0.057 1
 
< 0.1%
0.0572 1
 
< 0.1%
0.0574 2
 
< 0.1%
0.0579 1
 
< 0.1%
ValueCountFrequency (%)
0.991 1
 
< 0.1%
0.988 3
< 0.1%
0.987 2
 
< 0.1%
0.986 3
< 0.1%
0.985 6
< 0.1%
0.984 5
< 0.1%
0.983 2
 
< 0.1%
0.982 4
< 0.1%
0.981 1
 
< 0.1%
0.98 7
< 0.1%

energy
Real number (ℝ)

Distinct2571
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.54203599
Minimum0
Maximum1
Zeros33
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.5 MiB
2023-01-27T23:30:27.967676image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.12
Q10.343
median0.549
Q30.748
95-th percentile0.931
Maximum1
Range1
Interquartile range (IQR)0.405

Descriptive statistics

Standard deviation0.25192294
Coefficient of variation (CV)0.46477161
Kurtosis-0.96379157
Mean0.54203599
Median Absolute Deviation (MAD)0.202
Skewness-0.13138282
Sum317997.34
Variance0.063465168
MonotonicityNot monotonic
2023-01-27T23:30:28.085797image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.526 847
 
0.1%
0.538 846
 
0.1%
0.716 836
 
0.1%
0.448 835
 
0.1%
0.497 832
 
0.1%
0.53 826
 
0.1%
0.534 826
 
0.1%
0.666 823
 
0.1%
0.726 821
 
0.1%
0.499 820
 
0.1%
Other values (2561) 578360
98.6%
ValueCountFrequency (%)
0 33
< 0.1%
1.97 × 10-52
 
< 0.1%
1.98 × 10-51
 
< 0.1%
1.99 × 10-52
 
< 0.1%
2 × 10-53
 
< 0.1%
2.01 × 10-510
 
< 0.1%
2.02 × 10-512
 
< 0.1%
2.03 × 10-536
< 0.1%
2.8 × 10-51
 
< 0.1%
3.05 × 10-51
 
< 0.1%
ValueCountFrequency (%)
1 64
 
< 0.1%
0.999 217
< 0.1%
0.998 223
< 0.1%
0.997 245
< 0.1%
0.996 255
< 0.1%
0.995 312
0.1%
0.994 267
< 0.1%
0.993 256
< 0.1%
0.992 262
< 0.1%
0.991 311
0.1%

key
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2216025
Minimum0
Maximum11
Zeros74950
Zeros (%)12.8%
Negative0
Negative (%)0.0%
Memory size4.5 MiB
2023-01-27T23:30:28.195105image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.5194231
Coefficient of variation (CV)0.67401207
Kurtosis-1.2659395
Mean5.2216025
Median Absolute Deviation (MAD)3
Skewness-0.0013936386
Sum3063368
Variance12.386339
MonotonicityNot monotonic
2023-01-27T23:30:28.273231image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 74950
12.8%
7 73779
12.6%
2 66552
11.3%
9 65128
11.1%
5 53614
9.1%
4 48220
8.2%
1 41736
7.1%
11 39132
6.7%
10 37710
6.4%
8 33460
5.7%
Other values (2) 52391
8.9%
ValueCountFrequency (%)
0 74950
12.8%
1 41736
7.1%
2 66552
11.3%
3 21535
 
3.7%
4 48220
8.2%
5 53614
9.1%
6 30856
5.3%
7 73779
12.6%
8 33460
5.7%
9 65128
11.1%
ValueCountFrequency (%)
11 39132
6.7%
10 37710
6.4%
9 65128
11.1%
8 33460
5.7%
7 73779
12.6%
6 30856
5.3%
5 53614
9.1%
4 48220
8.2%
3 21535
 
3.7%
2 66552
11.3%

loudness
Real number (ℝ)

Distinct29196
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-10.206067
Minimum-60
Maximum5.376
Zeros0
Zeros (%)0.0%
Negative586453
Negative (%)> 99.9%
Memory size4.5 MiB
2023-01-27T23:30:28.374951image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-60
5-th percentile-19.843
Q1-12.891
median-9.243
Q3-6.482
95-th percentile-3.91
Maximum5.376
Range65.376
Interquartile range (IQR)6.409

Descriptive statistics

Standard deviation5.0893279
Coefficient of variation (CV)-0.49865712
Kurtosis2.7175721
Mean-10.206067
Median Absolute Deviation (MAD)3.095
Skewness-1.2359834
Sum-5987613.6
Variance25.901258
MonotonicityNot monotonic
2023-01-27T23:30:28.483793image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-8.026 116
 
< 0.1%
-5.797 95
 
< 0.1%
-4.47 95
 
< 0.1%
-5.584 81
 
< 0.1%
-7.348 80
 
< 0.1%
-6.484 79
 
< 0.1%
-8.871 78
 
< 0.1%
-6.651 78
 
< 0.1%
-7.016 78
 
< 0.1%
-7.031 78
 
< 0.1%
Other values (29186) 585814
99.9%
ValueCountFrequency (%)
-60 27
< 0.1%
-57.093 1
 
< 0.1%
-55 1
 
< 0.1%
-54.837 1
 
< 0.1%
-54.376 1
 
< 0.1%
-53.986 1
 
< 0.1%
-53.598 1
 
< 0.1%
-51.8 1
 
< 0.1%
-50.174 1
 
< 0.1%
-49.328 1
 
< 0.1%
ValueCountFrequency (%)
5.376 1
< 0.1%
5.109 1
< 0.1%
4.584 1
< 0.1%
4.362 1
< 0.1%
4.11 1
< 0.1%
3.855 1
< 0.1%
3.744 1
< 0.1%
3.575 1
< 0.1%
3.498 1
< 0.1%
3.273 1
< 0.1%

mode
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.5 MiB
1
386498 
0
200174 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters586672
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 386498
65.9%
0 200174
34.1%

Length

2023-01-27T23:30:28.583314image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-27T23:30:28.665147image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1 386498
65.9%
0 200174
34.1%

Most occurring characters

ValueCountFrequency (%)
1 386498
65.9%
0 200174
34.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 586672
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 386498
65.9%
0 200174
34.1%

Most occurring scripts

ValueCountFrequency (%)
Common 586672
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 386498
65.9%
0 200174
34.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 586672
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 386498
65.9%
0 200174
34.1%

speechiness
Real number (ℝ)

Distinct1655
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.10486354
Minimum0
Maximum0.971
Zeros329
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size4.5 MiB
2023-01-27T23:30:28.747733image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0276
Q10.034
median0.0443
Q30.0763
95-th percentile0.422
Maximum0.971
Range0.971
Interquartile range (IQR)0.0423

Descriptive statistics

Standard deviation0.17989279
Coefficient of variation (CV)1.7154941
Kurtosis13.417449
Mean0.10486354
Median Absolute Deviation (MAD)0.0133
Skewness3.6939506
Sum61520.504
Variance0.032361417
MonotonicityNot monotonic
2023-01-27T23:30:28.874003image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0312 2002
 
0.3%
0.033 1997
 
0.3%
0.0332 1990
 
0.3%
0.0308 1990
 
0.3%
0.0324 1979
 
0.3%
0.0309 1974
 
0.3%
0.0326 1973
 
0.3%
0.0319 1972
 
0.3%
0.0311 1970
 
0.3%
0.0313 1962
 
0.3%
Other values (1645) 566863
96.6%
ValueCountFrequency (%)
0 329
0.1%
0.0216 2
 
< 0.1%
0.0218 2
 
< 0.1%
0.022 2
 
< 0.1%
0.0221 6
 
< 0.1%
0.0222 7
 
< 0.1%
0.0223 17
 
< 0.1%
0.0224 10
 
< 0.1%
0.0225 18
 
< 0.1%
0.0226 18
 
< 0.1%
ValueCountFrequency (%)
0.971 3
 
< 0.1%
0.97 7
 
< 0.1%
0.969 25
 
< 0.1%
0.968 37
 
< 0.1%
0.967 41
 
< 0.1%
0.966 92
 
< 0.1%
0.965 117
< 0.1%
0.964 161
< 0.1%
0.963 230
< 0.1%
0.962 249
< 0.1%

acousticness
Real number (ℝ)

Distinct5217
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.44986272
Minimum0
Maximum0.996
Zeros66
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.5 MiB
2023-01-27T23:30:28.986020image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.00177
Q10.0969
median0.422
Q30.785
95-th percentile0.983
Maximum0.996
Range0.996
Interquartile range (IQR)0.6881

Descriptive statistics

Standard deviation0.3488367
Coefficient of variation (CV)0.77542922
Kurtosis-1.4661743
Mean0.44986272
Median Absolute Deviation (MAD)0.3403
Skewness0.15116105
Sum263921.86
Variance0.12168704
MonotonicityNot monotonic
2023-01-27T23:30:29.100574image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.995 4610
 
0.8%
0.994 3574
 
0.6%
0.993 2913
 
0.5%
0.992 2490
 
0.4%
0.991 2320
 
0.4%
0.99 2091
 
0.4%
0.989 1916
 
0.3%
0.988 1685
 
0.3%
0.987 1581
 
0.3%
0.996 1575
 
0.3%
Other values (5207) 561917
95.8%
ValueCountFrequency (%)
0 66
< 0.1%
1 × 10-61
 
< 0.1%
1.01 × 10-63
 
< 0.1%
1.03 × 10-62
 
< 0.1%
1.04 × 10-62
 
< 0.1%
1.05 × 10-62
 
< 0.1%
1.06 × 10-62
 
< 0.1%
1.07 × 10-63
 
< 0.1%
1.08 × 10-62
 
< 0.1%
1.09 × 10-61
 
< 0.1%
ValueCountFrequency (%)
0.996 1575
 
0.3%
0.995 4610
0.8%
0.994 3574
0.6%
0.993 2913
0.5%
0.992 2490
0.4%
0.991 2320
0.4%
0.99 2091
0.4%
0.989 1916
0.3%
0.988 1685
 
0.3%
0.987 1581
 
0.3%

instrumentalness
Real number (ℝ)

Distinct5402
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.11345078
Minimum0
Maximum1
Zeros205083
Zeros (%)35.0%
Negative0
Negative (%)0.0%
Memory size4.5 MiB
2023-01-27T23:30:32.875608image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2.45 × 10-5
Q30.00955
95-th percentile0.874
Maximum1
Range1
Interquartile range (IQR)0.00955

Descriptive statistics

Standard deviation0.26686787
Coefficient of variation (CV)2.3522788
Kurtosis3.5472102
Mean0.11345078
Median Absolute Deviation (MAD)2.45 × 10-5
Skewness2.2703983
Sum66558.397
Variance0.07121846
MonotonicityNot monotonic
2023-01-27T23:30:32.981589image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 205083
35.0%
0.911 410
 
0.1%
0.904 402
 
0.1%
0.916 402
 
0.1%
0.905 399
 
0.1%
0.917 399
 
0.1%
0.912 396
 
0.1%
0.901 396
 
0.1%
0.888 392
 
0.1%
0.897 387
 
0.1%
Other values (5392) 378006
64.4%
ValueCountFrequency (%)
0 205083
35.0%
1 × 10-6140
 
< 0.1%
1.01 × 10-6261
 
< 0.1%
1.02 × 10-6257
 
< 0.1%
1.03 × 10-6272
 
< 0.1%
1.04 × 10-6271
 
< 0.1%
1.05 × 10-6241
 
< 0.1%
1.06 × 10-6231
 
< 0.1%
1.07 × 10-6262
 
< 0.1%
1.08 × 10-6239
 
< 0.1%
ValueCountFrequency (%)
1 22
< 0.1%
0.999 17
< 0.1%
0.998 9
 
< 0.1%
0.997 15
< 0.1%
0.996 11
< 0.1%
0.995 14
< 0.1%
0.994 18
< 0.1%
0.993 22
< 0.1%
0.992 21
< 0.1%
0.991 23
< 0.1%

liveness
Real number (ℝ)

Distinct1782
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.21393502
Minimum0
Maximum1
Zeros43
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.5 MiB
2023-01-27T23:30:33.084803image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0589
Q10.0983
median0.139
Q30.278
95-th percentile0.653
Maximum1
Range1
Interquartile range (IQR)0.1797

Descriptive statistics

Standard deviation0.1843256
Coefficient of variation (CV)0.8615962
Kurtosis4.2887807
Mean0.21393502
Median Absolute Deviation (MAD)0.058
Skewness2.0448023
Sum125509.68
Variance0.033975926
MonotonicityNot monotonic
2023-01-27T23:30:33.183743image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.111 5579
 
1.0%
0.11 5310
 
0.9%
0.109 5173
 
0.9%
0.108 5162
 
0.9%
0.107 4946
 
0.8%
0.112 4834
 
0.8%
0.106 4788
 
0.8%
0.105 4674
 
0.8%
0.104 4592
 
0.8%
0.103 4442
 
0.8%
Other values (1772) 537172
91.6%
ValueCountFrequency (%)
0 43
< 0.1%
0.00572 1
 
< 0.1%
0.00838 1
 
< 0.1%
0.00967 1
 
< 0.1%
0.00986 1
 
< 0.1%
0.00989 1
 
< 0.1%
0.0101 1
 
< 0.1%
0.0108 2
 
< 0.1%
0.0111 2
 
< 0.1%
0.0112 3
 
< 0.1%
ValueCountFrequency (%)
1 4
 
< 0.1%
0.999 4
 
< 0.1%
0.998 4
 
< 0.1%
0.997 13
 
< 0.1%
0.996 12
 
< 0.1%
0.995 17
 
< 0.1%
0.994 21
< 0.1%
0.993 19
< 0.1%
0.992 33
< 0.1%
0.991 46
< 0.1%

valence
Real number (ℝ)

Distinct1805
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.55229247
Minimum0
Maximum1
Zeros369
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size4.5 MiB
2023-01-27T23:30:33.285429image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.121
Q10.346
median0.564
Q30.769
95-th percentile0.946
Maximum1
Range1
Interquartile range (IQR)0.423

Descriptive statistics

Standard deviation0.25767094
Coefficient of variation (CV)0.46654798
Kurtosis-1.0372164
Mean0.55229247
Median Absolute Deviation (MAD)0.211
Skewness-0.15230595
Sum324014.53
Variance0.066394312
MonotonicityNot monotonic
2023-01-27T23:30:33.384178image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.961 2679
 
0.5%
0.962 2312
 
0.4%
0.963 2023
 
0.3%
0.964 1846
 
0.3%
0.96 1651
 
0.3%
0.965 1599
 
0.3%
0.966 1489
 
0.3%
0.967 1349
 
0.2%
0.968 1155
 
0.2%
0.969 948
 
0.2%
Other values (1795) 569621
97.1%
ValueCountFrequency (%)
0 369
0.1%
1 × 10-5108
 
< 0.1%
6.41 × 10-51
 
< 0.1%
0.000183 1
 
< 0.1%
0.000562 1
 
< 0.1%
0.000998 1
 
< 0.1%
0.00123 1
 
< 0.1%
0.00128 1
 
< 0.1%
0.00142 1
 
< 0.1%
0.00155 1
 
< 0.1%
ValueCountFrequency (%)
1 14
< 0.1%
0.999 3
 
< 0.1%
0.997 5
 
< 0.1%
0.996 7
 
< 0.1%
0.995 6
 
< 0.1%
0.994 12
< 0.1%
0.993 5
 
< 0.1%
0.992 15
< 0.1%
0.991 19
< 0.1%
0.99 27
< 0.1%

tempo
Real number (ℝ)

Distinct122706
Distinct (%)20.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean118.46486
Minimum0
Maximum246.381
Zeros328
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size4.5 MiB
2023-01-27T23:30:33.483543image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile75.92355
Q195.6
median117.384
Q3136.321
95-th percentile174.00845
Maximum246.381
Range246.381
Interquartile range (IQR)40.721

Descriptive statistics

Standard deviation29.764108
Coefficient of variation (CV)0.25124842
Kurtosis-0.063967333
Mean118.46486
Median Absolute Deviation (MAD)20.601
Skewness0.40326627
Sum69500014
Variance885.90211
MonotonicityNot monotonic
2023-01-27T23:30:33.584950image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 328
 
0.1%
128.003 98
 
< 0.1%
119.994 91
 
< 0.1%
139.98 89
 
< 0.1%
127.994 86
 
< 0.1%
127.997 85
 
< 0.1%
128.01 82
 
< 0.1%
119.993 82
 
< 0.1%
127.999 81
 
< 0.1%
120 81
 
< 0.1%
Other values (122696) 585569
99.8%
ValueCountFrequency (%)
0 328
0.1%
30.506 1
 
< 0.1%
30.946 1
 
< 0.1%
31.21 1
 
< 0.1%
31.262 1
 
< 0.1%
31.29 1
 
< 0.1%
31.69 1
 
< 0.1%
31.988 1
 
< 0.1%
32.163 1
 
< 0.1%
32.205 1
 
< 0.1%
ValueCountFrequency (%)
246.381 1
< 0.1%
243.759 1
< 0.1%
243.507 1
< 0.1%
243.372 1
< 0.1%
240.782 1
< 0.1%
239.906 1
< 0.1%
238.895 1
< 0.1%
236.799 1
< 0.1%
236.134 1
< 0.1%
233.013 1
< 0.1%

time_signature
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.5 MiB
4
503808 
3
64523 
5
 
11400
1
 
6604
0
 
337

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters586672
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 503808
85.9%
3 64523
 
11.0%
5 11400
 
1.9%
1 6604
 
1.1%
0 337
 
0.1%

Length

2023-01-27T23:30:33.668296image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-27T23:30:33.751831image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
4 503808
85.9%
3 64523
 
11.0%
5 11400
 
1.9%
1 6604
 
1.1%
0 337
 
0.1%

Most occurring characters

ValueCountFrequency (%)
4 503808
85.9%
3 64523
 
11.0%
5 11400
 
1.9%
1 6604
 
1.1%
0 337
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 586672
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 503808
85.9%
3 64523
 
11.0%
5 11400
 
1.9%
1 6604
 
1.1%
0 337
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 586672
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 503808
85.9%
3 64523
 
11.0%
5 11400
 
1.9%
1 6604
 
1.1%
0 337
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 586672
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 503808
85.9%
3 64523
 
11.0%
5 11400
 
1.9%
1 6604
 
1.1%
0 337
 
0.1%

Interactions

2023-01-27T23:30:21.416788image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:02.365260image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:03.748477image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:05.190226image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:06.565088image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:08.090717image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:09.562731image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:11.003484image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:12.463092image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:13.910377image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:15.269030image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:16.670616image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:21.540783image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:02.466655image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:03.859812image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:05.306215image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:06.681276image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:08.212585image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:09.686529image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:11.135067image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:12.584738image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:14.025768image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:15.389258image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:16.791449image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:21.658514image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:02.593877image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:03.975869image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:05.420642image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:06.798931image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:08.347909image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:09.803694image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:11.251501image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:12.703498image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:14.135712image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:15.504583image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:16.908051image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:21.804144image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:02.712990image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:04.095844image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:05.533582image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:06.913018image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:08.467280image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:09.923205image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:11.367497image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:12.818336image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:14.248390image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:15.617699image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:17.024192image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:21.951618image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:02.825662image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:04.228674image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:05.650757image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:07.092044image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:08.578312image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:10.044685image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:11.489094image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:12.936949image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:14.362701image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:15.734146image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:17.148746image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:22.085128image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:02.941992image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:04.346157image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:05.762513image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:07.220387image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:08.694677image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:10.163298image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:11.653958image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:13.052586image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:14.473175image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:15.847354image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:17.260742image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:22.216992image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:03.062450image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:04.469811image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:05.881034image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:07.353700image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:08.817485image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:10.281854image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:11.773861image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:13.176619image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:14.588652image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:15.976231image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:17.378203image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:22.341700image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:03.176998image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:04.600288image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:05.993860image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:07.490816image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:08.936326image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:10.401661image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:11.890440image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:13.321268image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:14.698904image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:16.089160image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:17.490502image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:22.466248image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:03.294470image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:04.721636image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:06.106875image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:07.633478image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:09.057689image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:10.519241image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:12.006866image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:13.447218image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:14.810352image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:16.202120image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:17.603865image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:22.597131image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:03.409747image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:04.838916image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:06.221005image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:07.750052image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:09.186226image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:10.641311image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:12.120497image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:13.565669image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:14.920213image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:16.325014image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:21.049508image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:22.722179image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:03.522703image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:04.955606image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:06.335135image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:07.866609image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:09.322536image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:10.761898image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:12.234034image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:13.680095image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:15.032726image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:16.439722image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:21.168970image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:22.843008image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:03.636128image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:05.072230image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:06.447313image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:07.977992image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:09.443066image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:10.880605image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:12.347242image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:13.794299image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:15.149775image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:16.551819image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T23:30:21.291294image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-01-27T23:30:33.840626image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
popularityduration_msdanceabilityenergykeyloudnessspeechinessacousticnessinstrumentalnesslivenessvalencetempoexplicitmodetime_signature
popularity1.0000.1540.1800.3090.0150.351-0.023-0.368-0.236-0.0680.0040.0720.2460.0330.066
duration_ms0.1541.000-0.0980.1270.0120.104-0.177-0.1920.111-0.085-0.1870.0370.0170.0060.025
danceability0.180-0.0981.0000.2170.0180.1940.234-0.203-0.219-0.1300.507-0.0300.1950.0510.242
energy0.3090.1270.2171.0000.0360.7710.167-0.718-0.1260.0790.3600.2380.1380.0680.148
key0.0150.0120.0180.0361.0000.0280.028-0.027-0.001-0.0120.0190.0040.0570.2310.020
loudness0.3510.1040.1940.7710.0281.0000.021-0.529-0.2580.0180.2240.1820.1420.0470.190
speechiness-0.023-0.1770.2340.1670.0280.0211.000-0.038-0.1120.1160.1760.0420.3300.0500.146
acousticness-0.368-0.192-0.203-0.718-0.027-0.529-0.0381.0000.1110.018-0.155-0.2170.1510.0590.131
instrumentalness-0.2360.111-0.219-0.126-0.001-0.258-0.1120.1111.000-0.067-0.143-0.0120.0690.0130.033
liveness-0.068-0.085-0.1300.079-0.0120.0180.1160.018-0.0671.000-0.020-0.0180.0190.0140.041
valence0.004-0.1870.5070.3600.0190.2240.176-0.155-0.143-0.0201.0000.1290.0520.0320.100
tempo0.0720.037-0.0300.2380.0040.1820.042-0.217-0.012-0.0180.1291.0000.0530.0180.499
explicit0.2460.0170.1950.1380.0570.1420.3300.1510.0690.0190.0520.0531.0000.0520.056
mode0.0330.0060.0510.0680.2310.0470.0500.0590.0130.0140.0320.0180.0521.0000.020
time_signature0.0660.0250.2420.1480.0200.1900.1460.1310.0330.0410.1000.4990.0560.0201.000

Missing values

2023-01-27T23:30:23.171022image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-27T23:30:24.120410image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

idnamepopularityduration_msexplicitartistsid_artistsrelease_datedanceabilityenergykeyloudnessmodespeechinessacousticnessinstrumentalnesslivenessvalencetempotime_signature
04iJyoBOLtHqaGxP12qzhQIPeaches (feat. Daniel Caesar & Giveon)1001980821['Justin Bieber', 'Daniel Caesar', 'Giveon']['1uNFoZAHBGtllmzznpCI3s', '20wkVLutqVOYrc0kxFs7rA', '4fxd5Ee7UefO4CUXgwJ7IP']2021-03-190.6770.6960-6.18110.11900.321000.0000000.42000.46490.0304
17lPN2DXiMsVn7XUKtOW1CSdrivers license992420141['Olivia Rodrigo']['1McMsnEElThX1knmY4oliG']2021-01-080.5850.43610-8.76110.06010.721000.0000130.10500.132143.8744
23Ofmpyhv5UAQ70mENzB277Astronaut In The Ocean981327800['Masked Wolf']['1uU7g3DNSbsu0QjSEqZtEd']2021-01-060.7780.6954-6.86500.09130.175000.0000000.15000.472149.9964
35QO79kh1waicV47BqGRL3gSave Your Tears972156271['The Weeknd']['1Xyo4u8uXC1ZmMpatF05PJ']2020-03-200.6800.8260-5.48710.03090.021200.0000120.54300.644118.0514
46tDDoYIxWvMLTdKpjFkc1Btelepatía971601910['Kali Uchis']['1U1el3k54VvEUzo3ybLPlM']2020-12-040.6530.52411-9.01600.05020.112000.0000000.20300.55383.9704
57MAibcTli4IisCtbHKrGMhLeave The Door Open962420960['Bruno Mars', 'Anderson .Paak', 'Silk Sonic']['0du5cEVh5yTK9QJze8zA0C', '3jK9MiCrA42lLAdMGUZpwa', '6PvvGcCY2XtUcSRld1Wilr']2021-03-050.5860.6165-7.96410.03240.182000.0000000.09270.719148.0884
60VjIjW4GlUZAMYd2vXMi3bBlinding Lights962000400['The Weeknd']['1Xyo4u8uXC1ZmMpatF05PJ']2020-03-200.5140.7301-5.93410.05980.001460.0000950.08970.334171.0054
76f3Slt0GbA2bPZlz0aIFXNThe Business951640000['Tiësto']['2o5jDhtHVPhrJdv3cEQ99Z']2020-09-160.7980.6208-7.07900.23200.414000.0192000.11200.235120.0314
84cG7HUWYHBV6R6tHn1gxrlFriday (feat. Mufasa & Hypeman) - Dopamine Re-Edit941691530['Riton', 'Nightcrawlers', 'Mufasa & Hypeman', 'Dopamine']['7i9j813KFoSBMldGqlh2Z1', '1gALaWbNDnwS2ECV09sn2A', '4L2dV3zY7RmkeiNO035Fi0', '3Edve4VIATi0OZngclQlkN']2021-01-150.8240.8622-3.42410.12600.007600.0001320.30300.801122.9804
93FAJ6O0NOHQV8Mc5Ri6ENpHeartbreak Anniversary941983710['Giveon']['4fxd5Ee7UefO4CUXgwJ7IP']2020-03-270.4490.4650-8.96410.07910.524000.0000010.30300.54389.0873
idnamepopularityduration_msexplicitartistsid_artistsrelease_datedanceabilityenergykeyloudnessmodespeechinessacousticnessinstrumentalnesslivenessvalencetempotime_signature
5866620qDFFttZEtVFrTjelydcv9Bedard Zamana01769360['Pushpa Hans']['535DzRzak0t3zzaqxx7P2m']1949-12-310.5810.32206-10.78000.27700.9290.0000110.9610.836102.2524
5866630uXRv8f1z93RGOpiBjqxtXMand Mand Pawan Chale01767500['Saraswati Rane']['24GGycpacll4cru1CGrTId']1949-12-310.5220.32706-9.88010.07830.9900.8210000.2170.56785.9064
5866640uBhyG0vL3ilJiZgAhIwFgNera Nera Bandi01542670['Ghantasala']['2spmKkD1aaIwksnFrAghRL']1949-12-310.5640.44008-8.81110.07110.9950.0401000.1940.746109.0814
5866650tnNzcFbfglPBAgy93ZAb7Ek Nazar Woh Yaad Hai Unki01745650['Surinder Kaur']['5fucIZfxk9a3qSYc5nMkVC']1949-12-310.4940.19108-9.38600.03860.9940.0292000.4030.601132.2243
5866660rwy22SAQwi2PQXggEAyliPomp and Circumstance Marches, Op. 39: No. 2: Allegro molto03290670['Edward Elgar', 'Andrew Davis', 'Philharmonia Orchestra']['430byzy0c5bPn5opiu0SRd', '1Q9HCWdqbiui9pOsDtYkXW', '09KZU0NsS7jRa5p0SflmGY']19490.3190.239011-20.40100.04850.8040.8620000.3270.258122.2264
5866677cHgF1z7KeampXC54Vp0ddMy Baby Knows How01927010["Coon-Sanders' Original Nighthawk Orchestra"]['53E8i8Zawt25O5bICAORNO']19320.5250.47508-7.58110.16600.9930.1960000.2930.769209.4274
5866680uszXvZLZ2QpVJ45bkjDe9Satyi Katha Galpo Na01745200['Robin Majumdar']['76TzDmArcIh69amb9LzJff']1949-12-310.5590.202011-17.19010.14300.9940.8510000.1510.524125.2323
5866690GLyKUbNvQXoDPu6tWldbQO Come All Ye Faithful01392130['Julie Andrews']['5RdqZVi36tpDPYNPw8jJbO']19450.3940.22807-15.43510.03030.6960.0000000.3660.289104.0274
5866700ulNCe6co5KxRPdGqBv8TKAlegrias01594140['Carlos Montoya']['0fqQJD6wePdVDxuPUVrLyX']1949-07-090.4870.757010-13.43010.07390.9850.9010000.1400.46782.6254
5866714Jb8iDtQk8QgFFMHApIrgKKevin Barry01394930['The Clancy Brothers', 'Tommy Makem', "Paddy's Day"]['4qWTqOdDnH56Qak9UjmpKz', '3oehGAh6rLM6LFdzM7E7zM', '6k9ywduqO0afJNFBzlTktn']2015-06-290.4310.03259-17.96110.03440.9670.0000000.3550.391140.4343